


'''simple docstring'''
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_			:  Tuple										=   logging.get_logger(__name__)

UpperCAmelCase_			:  Optional[int]										=   {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    """sew-d"""


 def __init__(   self   ,   __lowercase=32   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase=2   ,   __lowercase=512   ,   __lowercase=256   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=("p2c", "c2p")   ,   __lowercase="layer_norm"   ,   __lowercase="gelu_python"   ,   __lowercase=0.1   ,   __lowercase=0.1   ,   __lowercase=0.1   ,   __lowercase=0.0   ,   __lowercase=0.1   ,   __lowercase=0.0_2   ,   __lowercase=1E-7   ,   __lowercase=1E-5   ,   __lowercase="group"   ,   __lowercase="gelu"   ,   __lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,   __lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,   __lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,   __lowercase=False   ,   __lowercase=128   ,   __lowercase=16   ,   __lowercase=True   ,   __lowercase=0.0_5   ,   __lowercase=10   ,   __lowercase=2   ,   __lowercase=0.0   ,   __lowercase=10   ,   __lowercase=0   ,   __lowercase="mean"   ,   __lowercase=False   ,   __lowercase=False   ,   __lowercase=256   ,   __lowercase=0   ,   __lowercase=1   ,   __lowercase=2   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase   ,   pad_token_id=__lowercase   ,   bos_token_id=__lowercase   ,   eos_token_id=__lowercase    )
  __A	:		Optional[Any]	  =      hidden_size
  __A	:		Any	  =      feat_extract_norm
  __A	:		Any	  =      feat_extract_activation
  __A	:		str	  =      list(__lowercase    )
  __A	:		Any	  =      list(__lowercase    )
  __A	:		int	  =      list(__lowercase    )
  __A	:		str	  =      conv_bias
  __A	:		str	  =      num_conv_pos_embeddings
  __A	:		Union[str, Any]	  =      num_conv_pos_embedding_groups
  __A	:		Optional[int]	  =      len(self.conv_dim    )
  __A	:		Optional[Any]	  =      num_hidden_layers
  __A	:		Union[str, Any]	  =      intermediate_size
  __A	:		List[Any]	  =      squeeze_factor
  __A	:		str	  =      max_position_embeddings
  __A	:		Union[str, Any]	  =      position_buckets
  __A	:		Dict	  =      share_att_key
  __A	:		Optional[Any]	  =      relative_attention
  __A	:		Any	  =      norm_rel_ebd
  __A	:		Tuple	  =      list(__lowercase    )
  __A	:		Optional[Any]	  =      hidden_act
  __A	:		Optional[Any]	  =      num_attention_heads
  __A	:		List[Any]	  =      hidden_dropout
  __A	:		Optional[Any]	  =      attention_dropout
  __A	:		Dict	  =      activation_dropout
  __A	:		List[Any]	  =      feat_proj_dropout
  __A	:		List[Any]	  =      final_dropout
  __A	:		Optional[int]	  =      layer_norm_eps
  __A	:		Tuple	  =      feature_layer_norm_eps
  __A	:		Dict	  =      initializer_range
  __A	:		List[Any]	  =      vocab_size

  if (
      (len(self.conv_stride    ) != self.num_feat_extract_layers)
      or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
      or (len(self.conv_dim    ) != self.num_feat_extract_layers)
  ):
   raise ValueError(
       'Configuration for convolutional layers is incorrect.'
       'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
       F"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
       F"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

  # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
  __A	:		Optional[int]	  =      apply_spec_augment
  __A	:		List[Any]	  =      mask_time_prob
  __A	:		Dict	  =      mask_time_length
  __A	:		int	  =      mask_time_min_masks
  __A	:		Any	  =      mask_feature_prob
  __A	:		List[str]	  =      mask_feature_length
  __A	:		Union[str, Any]	  =      mask_feature_min_masks

  # ctc loss
  __A	:		str	  =      ctc_loss_reduction
  __A	:		Union[str, Any]	  =      ctc_zero_infinity

  # sequence classification
  __A	:		Union[str, Any]	  =      use_weighted_layer_sum
  __A	:		Any	  =      classifier_proj_size







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return functools.reduce(operator.mul   ,   self.conv_stride   ,   1    )







"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
